google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Files (6)

• SKILL.md
• output-template.md · assets/
• attribution.md · references/
• design-recommendations.md · references/
• knowledge-catalog-documentation.md · references/
• product-selection-guidance.md · references/

SKILL.md (first lines)

# Agentic analytics across cloud providers and data types

This skill provides a workflow to design and implement a governed, secure
pipeline for agentic analytics solution across structured and unstructured data
that's distributed across Google Cloud, on-premises systems, and other cloud
providers.

## Attribution

**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you
MUST read and follow the instructions in
[Usage Attribution](references/attribution.md).

## Overview of the workflow

The workflow consists of the following phases:
*   **Phase 1: Requirements discovery**. Gather detailed requirements related to
    the cloud workload or use case that the user needs assistance for.
*   **Phase 2: Solution architecture**. Use the requirements that were gathered
    in Phase 1 to generate a detailed solution architecture for the cloud
    workload or use case.
*   **Phase 3: Solution validation**. Create a plan to validate the generated
    solution, generate validation instructions and scripts, and run the
    validation.
*   **Phase 4: Solution packing and presentation**. Consolidate the generated
    content and present the solution.

**Important notes about the workflow**:
*   **Strict phase separation**: During Phase 1 (Requirements discovery), when
    you ask the user clarifying questions, DON'T recommend, propose, or outline
    any architectural designs, technical decompositions, cloud services, or
    component mappings.
*   **When you can skip certain phases**: If the user's prompt indicates that a
    specific phase or task in this workflow is already completed or approved
    (e.g., "requirements discovery stage is completed", "product selection is
    approved", or "architecture is confirmed"), DON'T repeat that phase or task.
    Instead, skip directly to the requested task (such as generating the
    technical decomposition, recommending products, or compiling the solution
    guide).

## Phase 1: Requirements discovery and analysis

1.  Request the user to describe the functional requirements (business
    processes, activities, and use cases) of their workload. Ask the user the
    following questions, one question at a time:
    -   What are your primary inventory data sources? Are they unstructured
        (e.g., PDF flavor recipes, invoices) or structured (e.g., historical
        sales in Iceberg)?
    -   Where are these sources hosted? Are they split across AWS S3, Azure
        Blob, Google Cloud Storage, or databases like AlloyDB?
    -   How do you manage and federate metadata across your data
        sources within Google Cloud and in external locations (such as other
        cloud providers)?
    -   What are your analytical and computational requirements to join, clean,
        and run forecast models over large-scale distributed data?
    -   What types of natural language prompts do your data scientists or
        operational agents expect to execute in their agentic IDE (VS Code or
        Antigravity IDE)?
2.  Request the user to describe the non-functional requirements of their
    workload.

    The following are examples of questions you can ask to gather non-functional
    requirements:
    -   **Security, privacy, and compliance**: What data privacy rules,
        regulatory compliance (e.g., GDPR, HIPAA), or data governance
        requirements must the system adhere to?
    -   **Reliability**: What are your uptime, high-availability,
        fault-tolerance, and disaster recovery objectives (RTO/RPO)?
    -   **Performance**: What target query latencies and SLA expectations does
        your workload require?
    -   **Operations**: What operational monitoring metrics do your data
        scientists and engineers need?
    -   **Cost & Sustainability**: Do you have specific budget constraints and
        data egress/transfer cost requirements?
3.  Ask the user whether the workload currently runs on other cloud providers or
    on-premises.
    *   If the user answers "yes", then ask the user to describe the
        architecture of the current deployment.
    *   If the user answer "no", then proceed to the next step.
4.  Request the user to describe dependencies, if any, on other workloads,
    products, or tools. The following are examples of questions that you can
    ask to get information about the dependencies:
    *   Do you have any upstream or downstream dependencies on external systems
        (e.g., identity providers, data curation platforms, CI/CD pipelines, or
        active data catalogs)?
    *   Are there any requirements for your general data-engineering software
        delivery lifecycle (e.g., version control, testing, data quality
        assurance)? Provide the path to a directory or examples of these
        artifacts.
5.  Review the input that the user has provided so far, and check whether there
    are any ambiguities or contradictions.

    If you identify any ambiguities or contradictions in the requirements that
    the user has provided (e.g., zero-copy vs copying data to a repository), then
    do the following for each ambiguity or contradiction that you identify:
    *   Describe the ambiguity or contradiction (e.g., explain why copying data
        contradicts the zero-copy requirement and also incurs data-transfer
        costs).
    *   Ask the user how they wish to resolve the ambiguity or contradiction.
        *   If the user delegates the choice to you (e.g., the user replies with
            "do what you think is best" or "you decide"), then provide a clear
            suggestion to resolve the ambiguity or contradiction (e.g., suggest
            prioritizing zero-copy remote queries), explain your reasoning
            (e.g., to eliminate multi-cloud fees and data duplication), and ask
            the user to approve your suggestion.

    **Critical**: Until all the ambiguities and contradictions that you identify
    are resolved according to the preceding guidance, you must NOT recommend or
    generate any architecture design, technical decomposition, or Google Cloud
    product recommendations.

6.  **Important**: DON'T start this step if there are unresolved contradictions
    or ambiguities from Step 5.

    Generate a technical decomposition of the components of the workload.
    *   The technical decomposition must break down the solution into logical
        components.
    *   The decomposition MUST address role-based security and credentials
        within the relevant layers.

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